Highlights

• I experimentally study social learning in the simple two-armed bandit problem.
• Two- armed bandit setting allows me to confront different theories of social learning.
• Results are in line with counting heuristics, but not Bayesian-based reasoning.
• Results urge incorporation of count heuristics in the theory of social learning.
• Results helps to explain the technology, practice adoption failure, poor investments.

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Abstract: I conduct an experimental investigation of observational (social) learning in a simple two- armed bandit framework where the models are based on Bayesian reasoning and non- Bayesian count heuristics providing different predictions. The agents can choose between two alternatives with different probabilities of providing a reward. They must make their choice in order to see the outcome and act in a sequence. They can base their decision on the choices of the predecessors and the outcomes of their own choice. The results of the experiment follow neither Bayesian Nash Equilibrium nor Naïve herding model (BRTNI): Subjects follow and cascade on choices that contain no information about the state of the world, and, therefore, sustain losses when learning from others. I also test the Quantal response equilibrium and the robustness of this theory.

Igor Asanov, Bandit cascade: A test of observational learning in the bandit problem, Journal of Economic Behavior & Organization, Volume 189, 2021, Pages 150-171, ISSN 0167-2681, https://doi.org/10.1016/j.jebo.2021.06.006.
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• "A national experiment reveals where a growth mindset improves achievement"  by Yeagar and large group of coauthors (2019), Nature.  Extremely interesting paper on the effect of a short online growth mindset intervention on grades and enrolment to advanced mathematics among US school students.  From technical point of view, I found very interesting  the whole process of data analysis: "Confidence in the conclusions of this study comes from independent data collection and processing, pre-registration of analyses, and corroboration of results by a blinded Bayesian analysis."  (Yeagar et al., 2019) 

• "Civic honesty around the globe"  by Cohn et al. (2019), Science.  Puzzling result in large experiment across 40 countries. People are more likely to return the lost wallets with cash than without cash in it. Experts were not capable to predict these results. Further research is needed to better understand the reason for these results and generalisability of implication.

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• "A standardized citation metrics author database annotated for scientific field" by  Ioannidis et al. (2019), PLoS biology.  The study develops a large citation database and shows that a large number of the authors' citations comes from self-citations or citations by co-authors. In extreme cases, it can reach 94% raising the question to what extent citation indexes are accurate (see nice summary and discussion in Nature). The paper reminded me of the paper by Dominik Heinisch et al. (2016). They show that patterns of knowledge diffusion measured by citations of patents to non-patent literature changes drastically once one excludes individual and organizational self-citation. ​ Dominik is my co-author in another paper and we worked  in the same research group.  How fair is it for me to cite his paper? 

Highlights

• Reporting errors and reporting biases are relevant concerns for innovation research.
• Reporting errors are found in 45% of all articles and 4% of all tests.
• Discontinuities at conventional thresholds of statistical significance indicate reporting biases.
• Uncertainty due to rounding of published results is taken into account.

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Abstract: Errors and biases in published results compromise the reliability of empirical research, posing threats to the cumulative research process and to evidence-based decision making. We provide evidence on reporting errors and biases in innovation research. We find that 45% of the articles in our sample contain at least one result for which the provided statistical information is not consistent with reported significance levels. In 25% of the articles, at least one strong reporting error is diagnosed where a statistically non-significant finding becomes significant or vice versa using the common significance threshold of 0.1. The error rate at the test level is very small with 4.0% exhibiting any error and 1.4% showing strong errors. We also find systematically more marginally significant findings compared to marginally non-significant findings at the 0.05 and 0.1 thresholds of statistical significance. These discontinuities indicate the presence of reporting biases. Explorative analysis suggests that discontinuities are related to authors’ affiliations and to a lesser extent the article’s rank in the issue and the style of reporting. 

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